In this study, we propose a method for estimating human segmentation from airborne ultrasound detected by multichannel microphones using a neural network.
The concept of our work is illustrated in fig:concept.
In the sensing section, an ultrasonic transducer at 62 kHz resonance, and 16 channels of microelectromechanical system (MEMS) microphone array are used.
The ultrasound emitted by the ultrasonic transducer is reflected by a human body and then captured by microphones.
The reflected ultrasound directional images (hereinafter, ultrasound images) can be obtained by analyzing the differences between multichannel ultrasound signals.
Because the ultrasound image represents the intensity of the reflected wave at each pixel, it has ambiguous shapes that are far from humans.
Therefore, we introduce a deep neural network to obtain human segmentation images from ultrasound images.
We use probabilistic models for the task because the ultrasound images have different edge positions from segmentation images.
Kohl et al. [1]} proposed a probabilistic U-Net that combines variational autoencoder (VAE) [2]} and U-Net [3]}.
During the training phase, the probabilistic U-Net learns a network to match latent spaces of input and the input/ground truth by comparing the latent spaces with Kullback-Leibler divergence (KLD).
A latent vector, sampled from the latent space of input/ground truth, combined with the last activation map of a U-Net.
The spatial positions of the ultrasound and segmentation images are similar and the edges are different.
Hence, the latent distributions of ultrasound and segmentation images are roughly close because of shrinking the spatial dimensions by convolutions.
Thus, reducing the distance between latent distributions with precisely estimating the edges is difficult.
Therefore, we propose a collaborative learning probabilistic U-Net (CLPU-Net), which uses mean squared error (MSE) instead of KLD to minimize the distance between latent distributions.
Experiments showed that human segmentation images could be generated from ultrasound images.
To the best of our knowledge, this is the first work to estimate human segmentation images from airborne ultrasounds.
